Methods and apparatus to detect and rectify false set top box tuning data

ABSTRACT

Methods, apparatus, systems, and articles of manufacture are disclosed to rectify false set top box tuning data. Disclosed examples methods include identifying in return path data a first group of set top boxes classified as likely to exhibit machine events in tuning data of the return path data more frequently than a second group of set top boxes represented in the return path data. Additionally, in some examples, the method includes determining whether the first group of set top boxes includes a machine event based on a pattern of the tuning data in the return path data for respective ones of the first group of set top boxes and improving an accuracy of return path data by rectifying the machine event.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 15/820,104, entitled “METHODS AND APPARATUS TO DETECT AND RECTIFYFALSE SET TOP BOX TUNING DATA,” and filed on Nov. 21, 2017. U.S. patentapplication Ser. No. 15/820,104 is hereby incorporated herein byreference in its entirety. Priority to U.S. patent application Ser. No.15/820,104 is claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to processing set top box tuning data,and, more particularly, to methods and apparatus to detect and rectifyfalse set top box tuning data.

BACKGROUND

In recent years, televisions present media via a set top box.Media-centric companies such as, for example, advertising companies,broadcasting networks, etc., are often interested in the viewership ofbroadcasted media. In some examples, the set top box records and reportstuning data representing tuning events that track viewer activitiesincluding changing the channel, turning on/off the set top box, pausinga channel via a digital video recorder (DVR), etc. The set top boxtuning data provide a metric in determining the audience size for abroadcasted media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example media monitoring system including anexample audience measurement entity for receiving set top box tuningdata in accordance with the teachings of this disclosure.

FIG. 2 illustrates an example block diagram of an example audiencemeasurement entity for rectifying false set top box tuning data inaccordance with the teachings of this disclosure.

FIG. 3 is an example graph of set top box tuning data and panelist metertuning data indicating the presence of a machine event.

FIG. 4 is an example graph of tuning data indicating the presence of amachine event.

FIG. 5 is an example graph of tuning data indicating the identificationof the presence of a machine event.

FIG. 6 is a first example table of tuning data indicating a pattern oftuning data indicative of a machine event.

FIG. 7 is a second example table of tuning data indicating a pattern oftuning data indicative of a machine event.

FIG. 8 is a third example table of tuning data indicating a pattern oftuning data indicative of a machine event.

FIG. 9 is a flowchart representative of first example computer readableinstructions that may be executed to implement the example audiencemeasurement entity of FIG. 2.

FIG. 10 is a flowchart representative of second example computerreadable instructions that may be executed to implement the exampleaudience measurement entity of FIG. 2.

FIG. 11 is a block diagram of an example processor platform structuredto execute the example computer readable instructions of FIGS. 9 and/or10 to implement the example audience measurement entity of FIG. 2.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Example methods, apparatus, systems and articles of manufacture (e.g.,non-transitory, physical storage media) to detect and rectify false settop box tuning data are disclosed herein. Example methods disclosedherein to remove false tuning data include identifying in return pathdata a first group of set top boxes classified as likely to exhibitmachine events in tuning data of the return path data more frequentlythan a second group of set top boxes represented in the return pathdata. Disclosed example methods also include determining whether thefirst group of set top boxes includes a machine event based on a patternof the tuning data in the return path data of the first group of set topboxes. Disclosed example methods further include improving an accuracyof return path data by rectifying the machine event.

In some disclosed example methods the pattern of the tuning datacorresponds to a difference between a first percentage of the firstgroup of set top boxes having tuning events in the return path dataduring a first time interval and a second percentage of the second groupof set top boxes having tuning events in the return path data during thefirst interval. Some disclosed examples also include removing firsttuning data associated with the first time interval in the return pathdata corresponding to the first percentage of the first group of set topboxes.

Additionally or alternatively, in some disclosed example methods, thepattern of the tuning data includes a feature activated machine eventproducing false tuning data. In some such disclosed examples, therectifying of the machine event includes identifying and removing thefalse tuning data while maintaining veridical tuning data in the returnpath data. Additionally or alternatively, in some such disclosedexamples, the feature activated machine event is identified by aprerequisite of the feature activated machine event, the prerequisitedetermined by comparing return path data to panelist exposure dataobtained from meters monitoring exposure of media to a panelist. In somesuch disclosed examples, the panelist exposure data is classified asveridical.

These and other example methods, apparatus, systems and articles ofmanufacture (e.g., non-transitory, physical storage media) to detect andrectify false set top box tuning data are disclosed in further detailbelow.

Audience measurement entities (AMEs) measure a composition and size ofaudiences consuming media to produce ratings of the media. Ratings maybe used by advertisers and/or marketers to purchase advertising spaceand/or design advertising campaigns. Media producers and/or distributersmay use the rating to determine how to set prices for advertising spaceand/or to make programming decisions. To measure the composition andsize of an audience, AMEs (e.g., The Nielsen Company (US), LLC®) trackaudience members' exposure to media.

AMEs may enlist panelist households to participate in measurementpanels. Media exposure and/or demographics data associated with thepanelist households may be collected and may be used to project a sizeand demographic makeup of a population. Members of panelist householdsconsent to AMEs collecting exposure data by measuring exposure of thepanelist households to media (e.g., television programming, radioprogramming, online content, programs, advertising, etc.). As usedherein, “exposure data” refers to information pertaining to mediaexposure events presented via a media presentation device (e.g., atelevision, a stereo, a speaker, a computer, a portable device, a gamingconsole, an online media presentation device, etc.) of a panelisthousehold and associated with a person and/or group of persons of thehousehold (e.g., panelist(s), member(s) of the panelist household). Forexample, panelist exposure data is obtained from meters monitoringexposure of media presentations of a household. Exposure data includesinformation indicating that a panelist is exposed to a media if thepanelist is present in a room in which the media is being presented. Toenable the AMEs to collect such exposure data, the AMEs typicallyprovide panelist households with meter(s) that monitor mediapresentation devices (e.g., televisions, stereos, speakers, computers,portable devices, gaming consoles, and/or online media presentationdevices, etc.).

Enlisting and retaining panelists for audience measurement can be adifficult and costly process for AMEs. For example, AMEs must carefullyselect and screen panelist households for particular characteristics sothat a population of the panelist households is representative of thepopulation as a whole. Because collecting information from panelisthouseholds can be difficult and costly, AMEs and other entitiesinterested in measuring media/audiences have begun to collectinformation from other sources such as set-top boxes and/or over-the-topdevices (e.g., a Roku® media device, an Apple TV® media device, aSamsung Smart TV® media device, a Google TV™ platform, a GoogleChromecast™ device, an Amazon TV media device, a gaming console, a smartTV, a smart DVD player, an audio-streaming device, etc.). A set-top boxis a device that converts source signals into media presented via amedia presentation device. In some examples, the STB implements adigital video recorder (DVR) and/or a digital versatile disc (DVD)player. Further, some STBs are capable of recording tuning data ofcorresponding media presentation devices. As used herein, “tuning data”refers to information pertaining to tuning events (e.g., an STB beingturned on or off, channel changes, tuning duration times, etc.) of anSTB and/or a media presentation device of a household that is notassociated with demographics data (e.g., number of household members,age, gender, race, etc.) of the household and/or members of thehousehold.

Tuning data of the STB is collected by STB data providers, buthouseholds can opt out of this data collection (e.g., via processes of athird-party media provider and/or manufacturer, the AME, etc.). Manyhouseholds are willing to provide tuning data via an STB, becausepersonalized information is not collected by the STB. As used herein,households that consent to collection of tuning data (e.g., via an STB),but do not consent to collection of exposure data (e.g., media exposuredata that is tied to a particular person such as a panelist) arereferred to as “non-panelist households.” While collecting data fromnon-panelist households can greatly increase the amount of collecteddata about media exposure, the lack of exposure data reduces the valueof this media exposure data.

In some examples, return path data from the STB contains false tuningdata as a result of machine events. As used herein, “return path data”or “RPD” refers to tuning data collected at the STB and provided to theaudience measurement entity. Additionally, as used herein, a “machineevent” is an STB tuning event that is not directly initiated by aviewer. For example, the STB may undergo a software update that causesthe STB to register tuning events not correlated to a viewer's activity,such as the STB restarting after the software updates, or cyclingthrough channels upon completion of the update. In other examples, theSTB experiences a machine event in response to viewer tuning events(e.g., a feature activated machine event), such as logged phantom tuningevents after legitimate tuning events. For example, some machine eventshave a prerequisite (e.g., cause, precursor, etc.). In such examples, ifthe prerequisite of some feature activated machine events is known(e.g., a known feature activated machine event), and a pattern of tuningevents can be identified as a machine event based on the pattern oridentification of the prerequisite in RPD. Machine events reduce theaccuracy of RPD-based media measurement.

Detection and rectification of machine events, in accordance with theteachings of this disclosure, can improve the accuracy of RPD-basedmedia measurement. Rectification of machine events, including theremoval of false tuning data while retaining veridical tuning eventsand/or the removal of all data corresponding to time intervalsexperiencing machine events, occurs after machine events have beendetected and identified. Machine events are identified either based onknown feature activated machine events or other evaluation of RPDpatterns. In some examples, known feature activated machine events canbe corrected, while other RPD patterns can only be rectified by deletingtuning data.

For example, a first group of set top boxes can exhibit machine eventsmore frequently than a second group of set top boxes. In some examples,STBs from one media provider can exhibit machine events more than STBsfrom a different media provider. Additionally or alternatively, in someexamples, a type of STB from a provider (e.g., DVR capable) can exhibitmore machine events than a different type of STB from the same provider(e.g., non-DVR capable). Additionally or alternatively, in some examplesone model of STB (e.g., second generation DVR) can exhibit more machineevents than a different model of STB (e.g., first generation DVR) fromthe same provider. In accordance with the present disclosure, featureactivated machine events can be identified based on characteristics ofthe STB (e.g., media provider, type, model, viewing/recording features,etc.).

Without rectification of machine events, false RPD data can be combinedwith panelist data resulting in the inaccurate estimation of trends inthe tuning activity. Such methods are inaccurate and require excessivecomputer processing power and memory. However, rectification of machineevents improves the accuracy of STB tuning data, the value of STB tuningdata to media providers, and reduces the processor requirements andmemory storage of RPD.

FIG. 1 illustrates an example media monitoring system 100 including anexample audience measurement entity 102 for receiving and processingtuning data in accordance with the teachings of this disclosure. In theexample media monitoring system 100 of FIG. 1, the example audiencemeasurement entity 102 receives return path data (RPD) 104 from mediaproviders 110, 112, 114. For example, the media providers 110, 112, 114provide set top boxes (STBs) and media to viewers and collect tuningdata from viewers who agree to share tuning data. RPD 104 is the tuningdata collected by the media providers 110, 112, 114 and provided to theaudience measurement entity 102. In some examples, the media providers110, 112, 114 can operate in different geographical regions, while inother examples, the example media providers can compete in the samegeographical region.

In the illustrated example, the example media provider 110 provides STBsand media to households 120, 122, 124. In response, the households 120,122, 124, having agreed to share tuning data with the media provider110, send tuning data 126 to the media provider 110. In the illustratedexample, tuning data 126 is reported to the media provider 110 vianetwork communications. In the illustrated, the example household 120 isa panelist household and has agreed to send exposure data 128 to theexample audience measurement entity 102. Additionally or alternatively,media provider 112 provides STBs and media to households 130, 132, 134.In response, the households 130, 132, 134 provide tuning data 136 to themedia provider 112. In the illustrated example, the example household132 is a panelist household and has agreed to send exposure data 138. Inthe illustrated example, media provider 114 provide STBs and media tohouseholds 140, 142, 144. In response, the household 140, 142, 144provide tuning data 146 to the media provider 114. In the illustratedexample, the example household 140 provides exposure data 148 to theaudience measurement entity 102.

In the example of FIG. 1, the media providers 110, 112, 114 provide theRPD 104 to the audience measurement entity 102. In some examples, theRPD is transmitted over network communications, while in other examples,one RPD can be provided as a memory device delivered to the audiencemeasurement entity 102. In some examples, the RPD 104 includes falsetuning data incurred by machine events from one or more of the STBs fromhouseholds 120, 122, 124, 130, 132, 134, 140, 142, 144.

FIG. 2 illustrates an example block diagram of the example audiencemeasurement entity 102 of FIG. 1 for detecting and rectifying falsetuning data incurred by machine events in accordance with the teachingof this disclosure. In the illustrated example of FIG. 2, the audiencemeasurement entity 102 includes an example data receiver 205, an examplememory 210, an example set top box identifier 215, an example patternevaluator 220, an example tuning event calculator 225, and an examplefalse tuning event rectifier 230. In other examples, the audiencemeasurement entity 102 can include more components or fewer components.

The example data receiver 205 receives RPD from the example mediaproviders 110, 112, 114 (FIG. 1). In some examples, the RPD is receivedperiodically, a-periodically, and/or upon request by the audiencemeasurement entity 102. The example RPD received by the data receiver205 for a given STB includes information regarding the media provider,the model of STB, a record of tuning event(s), and timestamp(s)corresponding to the tuning event(s). In some examples, the RPD mayinclude information regarding the type(s) of tuning event(s) and/or thechannel(s) associated with the tuning event(s). The RPD is stored by theexample data receiver 205 in the example memory 210.

The memory 210 of FIG. 2, stores RPD until the data is ready to beprocessed. For example, tuning data may be stored in the example memory210 for up to two days (or some other duration) before being processedand evaluated. In other examples, processing the RPD can occurimmediately. Additionally or alternatively, the memory 210 can bedisposed separate from the audience measurement entity 102. In someexamples, RPD received at the example data receiver 205 is configuredfor storage in the example memory 210. For example, informationregarding the media provider, set top box model, and the tuning data canbe organized for utilization by the audience measurement entity 102. Inother examples, the RPD is stored in the memory 210 as provided by themedia providers 110, 112, 114.

In the illustrated example, the example audience measurement entity 102includes the example set top box identifier 215. The set top boxidentifier 215 accesses RPD to associate tuning data stored in memorywith features of STBs providing the tuning data. For example, the settop box identifier 215 can identify a media provider associated withtuning data, a set top box model associated with tuning data, and/or atype of set top box associated with tuning data. Certain models andtypes of STB exhibit more machine events than other models and types ofSTB. As a result, the example set top box identifier 215 can furtherflag those STBs associated with the types and models that are likely toexhibit machine events. Additionally or alternatively, the set top boxidentifier 215 can associate tuning data with a given region, time zone,and/or other identifying characteristics of a STB.

The example pattern evaluator 220 evaluates patterns in the RPD formodels and types of STB that exhibit machine events as identified by theset top box identifier 215. For example, some models and types of STBhave identified patterns of machine events that produce false tuningevents (e.g., such as examples in which activation of a record featurehas been determined to produce a subsequent false tuning event of agiven type, switching to certain channels at certain times has beendetermined to produce a subsequent false tuning event of a given type,etc.). In such examples, the pattern evaluator 220 evaluates data formodels and types of STB that exhibit known machine events. For example,if model X produces false tuning data when the viewer switches tochannel 1 after 7:00 pm, the example pattern evaluator 220 evaluatestuning data associated with model X STBs and flags all tuning dataresults from viewers switching to channel 1 after 7:00 pm as false. Theexample pattern evaluator 220 can evaluate RPD for any known pattern ofmachine events and appropriately flag the machine events. In someexamples, the pattern evaluator 220 can determine if a percentage oftuning events for a given STB exceeds a threshold for a sufficient timeinterval.

The example tuning event calculator 225 calculates a percentage of settop boxes having tuning events starting or ending for a given timerelative to a total number of set top boxes capable of having tuningevents for the given time. For example, the tuning event calculator 225calculates, based on the identification of the set top box identifier215, the percentage of DVR capable set top boxes from the media provider110 (FIG. 1) have a tuning event for a time (e.g., 07:36:25, 15:23:53,etc.) relative to a total number of DVR capable set top boxes theexample media provider 110 has provided. In some examples, the tuningevent calculator 225 calculates the percentage of a set top boxes havinga model number or provided by a media provider. In some examples, thepercentage of a certain model or type of STB, which exhibits machineevents, having tuning data at a given time can be compared to acomparable model, compared to a comparable type, or compared to RPDtuning data overall to identify surges in RPD tuning data. If adifference or ratio of tuning events from STBs classified as likely toexhibit machine events compared to tuning events from STBs classified asnot likely to exhibit machine events satisfies a threshold for a giventime interval (e.g., 3 minutes, 5 minutes), the tuning calculator 225flags those minutes that satisfy the threshold as including false tuningdata as a result of machine events.

In the illustrated example, the audience measurement entity 102 alsoincludes the example false tuning event rectifier 230. In some examples,the false tuning event rectifier removes only the false tuning data.However, in some examples, the false tuning event rectifier removesfalse tuning data along with veridical tuning data. For example, if theexample pattern evaluator 220 flagged tuning data as false tuning dataas a result of a machine event, the false tuning event rectifier 230 canremove or correct the false tuning data because the veridical tuningevent and the false tuning event are both known based on the pattern. Inother examples, the false tuning event rectifier 230 removes falsetuning data without being able to distinguish from veridical tuningevents or false tuning events, and, thus, removes both veridical andfalse tuning events for the flagged minutes.

FIG. 3 is an example graph of tuning data 300 of set top box tuning data305 and audience measurement entity (AME) tuning data 310 (e.g.,panelist meter tuning data) indicating the presence of a machine event.In the illustrated example, the STB tuning data 305 and the AME data 310are substantially similar, with the exception of a surge in tuningevents 315 in the STB tuning data 305 beginning just before 9:00 p.m.The surge in tuning events 315 skews the value of STB tuning data 305.In the illustrated example, the accuracy of the STB tuning data 305 canbe improved by removing the surge in tuning events 315 from the STBtuning data 305.

FIG. 4 is an example graph of tuning data 400 for a day indicating thepresence of a machine event. The example graph of tuning data 400includes one day of tuning data, from 3:00 a.m. to 3:00 a.m. Similar tothe example graph of tuning data 300 of FIG. 3, the graph of tuning data400 shows another surge in tuning data 405 that skews the value of STBtuning data. In the illustrated example, the surge in tuning data 405shows the irregularity some machine events can exhibit. While the surgein tuning events 315 of FIG. 3 corresponded to an increase in actualtuning events, the surge in tuning events 405 stands in stark contrastto the quantity of tuning events in the surrounding hours of RPD.

FIG. 5 is an example graph of tuning data 500 indicating theidentification of the presence of a machine event 505. In theillustrated example, the set top box identifier 215 of FIG. 2 hasidentified a first group of set top boxes 510 and a second group of settop boxes 520. The first group of STBs 510 have features used toclassify the STBs 510 as being likely to exhibit machine events, whereasthe second group of STBs 520 have features used to classify the STBs 520as being unlikely to exhibit machine events. In some examples, theexample graph of tuning data 500 can evaluate additional groups of STBsclassified as being likely or unlikely to exhibit machine events. Theexample machine event 505 is a feature activated machine event, whichwill be described in further detail in FIG. 6, but the machine event 505could also not be associated with any known pattern. Additionally, inthe illustrated example, the pattern evaluator 220 and/or the tuningevent calculator 225 identify a flagged time interval 530.

In some examples, if the example first group of STBs 510 exhibit knownfeature activated machine events (e.g., a recognizable patternassociated with machine events), then the pattern evaluator 220 (FIG. 2)can evaluate the tuning data for the first group of STBs 510 for a knownfeature activated machine events. For example, if the event 505corresponds to a known feature activated machine event, the examplepattern evaluator 220 identifies the flagged time interval 530 (e.g., 30seconds, 5 minutes) that includes the recognized pattern of the knownfeature activated machine event. However, in some examples, the examplefirst group of STBs 510 exhibit machine events that do not follow arecognizable pattern. In some such examples, the tuning event calculator225 (FIG. 2) determines a difference (e.g., expressed as a ratio or someother comparison value) of the percentage of tuning events for the firstgroup of STBs 510 to the second group of STBs 520. The difference (e.g.,ratio) between the tuning events of the first group of STBs 510 and thesecond group of STBs 520 is compared to a threshold. If the threshold issatisfied for a sufficient period of time, such as corresponding to theevent 505, the example tuning event calculator 225 (FIG. 2) flags thetime interval 530 during which the threshold is satisfied.

In some examples, the threshold may vary. For example, during some partsof the day tuning data may be naturally erratic (e.g., morning news,primetime, etc.), while in other parts of the day tuning may benaturally more stable (e.g., daytime, overnight). As a result, thethreshold may be higher during the erratic times (e.g., 200 percentagepoint (pp) difference, 300 pp difference, or some other value).Additionally or alternatively, the threshold may be lower during themore stable times (e.g., 50 pp difference, 125 pp difference, or someother value).

FIG. 6 is an example table of tuning data 600 indicating a pattern oftuning data indicative of a machine event. In the illustrated example ofFIG. 6, a feature activated event start time 605 corresponds to afollow-up tuning event start time 610. In the illustrated example, 99.2%of follow-up tuning events start within 7 seconds of the featureactivated event start time. The time pattern of the feature activatedevent start time 605 and the follow-up tuning event start time 610 is arecognizable pattern to identify false tuning. For example, some STBsinclude follow-up tuning to a channel different from the channel for theviewing feature after the start of a recording feature. In someexamples, the follow-up tuning channel is consistently the same acrossSTBs having a similar model, type, or feature. The feature and follow-upevent start times are identifiable based on the time proximity of theevents (e.g., less than 10 seconds, less than 30 seconds) and therepeated channel change. Such a machine event can be identified in asingle STB and verified across a group of STBs having a similar model,type and/or feature. Additionally or alternatively, some feature andfollow-up events may have different station IDs, making identificationof the false tuning data easier. In some examples, guide updates,viewing/recording features, viewing recorded programs, video downloads,pay-per-view, software updates, and/or other features can be followed befalse follow-up tuning events.

FIG. 7 is another example table of tuning data 700 indicating a patternof tuning data indicative of a machine event that can be identified bycomparing RPD to panelist exposure data for the same STB. In someexamples, STBs will also correspond to panelists included in an AME'sexposure data. The AME's exposure data for such STBs can be refered toas common home data. In some such examples, the common home data for anSTB included in both the RPD and AME exposure data can be used toidentify machine events in the RPD. In the illustrated example of FIG.7, a set top box exhibits a machine event and produces a false tuningevent 705 in the RPD. In some examples, the machine event was a resultof the time of day, tuning to a specific station channel 710, or acombination of tuning to the specific station channel at the particulartime of day. In the illustrated example, comparison of the stationchannel 710 and the AME device station 720 reported in the AME exposuredata for that same STB is used to determine the pattern of featureactivated machine events. In the illustrated example, after a viewerchanges the channel at 730, the STB stops producing false tuning data inthe RPD.

FIG. 8 is an example table of tuning data 800 indicating a pattern oftuning data indicative of a machine event. In the illustrated example ofFIG. 8, an STB exhibits a machine event during a use of a “Timer record”function. The example table of tuning data 800 indicates a pattern ofstation channel switching at regular timing intervals as a result of therecording feature. In the illustrated example, comparison of the stationchannel 810 and the AME device station 820 in the AME exposure data forthe same STB is used to determine the pattern of feature activatedmachine events.

In the illustrated examples of FIGS. 7 and 8, the patterns of falsetuning data are determined by comparing STB tuning data to panelistexposure data available for the same STB(s) (e.g., referred to as commonhome data). In the example of FIG. 7, the pattern of tuning data isindicative of a machine event when either the station channel 710reported in the RPD switches from 4555 to 5894, but the station channelreported in the AME exposure data (e.g., which is generally morereliable) for the same STB remains on 5894. Thus, the channel changereported in the RPD for the STB corresponds to a false tuning event. Insome examples, STB tuning data indicates patterns of tuning data when nopanelist exposure data is collected from a panelist meter. For example,if the meter obtains no panelist exposure data while the RPD tuning datafor the same STB indicates a regular alternating pattern of “LIVE TUNE”and “TIMER RECORD,” that pattern may be identified as a possible patternassociated with a machine event. If the same pattern is regularly foundin RPD as false tuning data, the pattern of tuning data can beclassified as a machine event. As a result, tuning data having a patternnot consistent with panelist exposure data can be identified andrectified in RPD.

While an example manner of implementing the audience measurement entityof FIG. 1 is illustrated in FIGS. 2-8, one or more of the elements,processes and/or devices illustrated in FIG. 2-8 may be combined,divided, re-arranged, omitted, eliminated, and/or implemented in anyother way. Further, the example data receiver 205, the example memory210, the example set top box identifier 215, the example patternevaluator 220, the example tuning event calculator 225, the examplefalse tuning event rectifier 230 and/or, more generally, the exampleaudience measurement entity 102 of FIG. 2 may be implemented byhardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example datareceiver 205, the example memory 210, the example set top box identifier215, the example pattern evaluator 220, the example tuning eventcalculator 225, the example false tuning event rectifier 230 and/or,more generally, the example audience measurement entity 102 of FIG. 2could be implemented by one or more analog or digital circuit(s), logiccircuits, programmable processor(s), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example, datareceiver 205, the example memory 210, the example set top box identifier215, the example pattern evaluator 220, the example tuning eventcalculator 225, the example false tuning event rectifier 230 is/arehereby expressly defined to include a non-transitory computer readablestorage device or storage disk such as a memory, a digital versatiledisk (DVD), a compact disk (CD), a Blu-ray disk, etc. including thesoftware and/or firmware. Further still, the example audiencemeasurement entity of FIG. 1 may include one or more elements, processesand/or devices in addition to, or instead of, those illustrated in FIGS.2-8, and/or may include more than one of any or all of the illustratedelements, processes, and devices.

Flowcharts representative of example machine readable instructions forimplementing the audience measurement entity 102 of FIGS. 1 and/or 2 areshown in FIGS. 9-10. In these examples, the machine readableinstructions comprise a program for execution by a processor such as theprocessor 1112 shown in the example processor platform 1100 discussedbelow in connection with FIG. 11. The program may be embodied insoftware stored on a non-transitory computer readable storage mediumsuch as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk(DVD), a Blu-ray disk, or a memory associated with the processor 1112,but the entire program and/or parts thereof could alternatively beexecuted by a device other than the processor 1112 and/or embodied infirmware or dedicated hardware. Further, although the example program isdescribed with reference to the flowcharts illustrated in FIG. 9-10,many other methods of implementing the example audience measuremententity 102 may alternatively be used. For example, the order ofexecution of the blocks may be changed, and/or some of the blocksdescribed may be changed, eliminated, or combined. Additionally oralternatively, any or all of the blocks may be implemented by one ormore hardware circuits (e.g., discrete and/or integrated analog and/ordigital circuitry, a Field Programmable Gate Array (FPGA), anApplication Specific Integrated circuit (ASIC), a comparator, anoperational-amplifier (op-amp), a logic circuit, etc.) structured toperform the corresponding operation without executing software orfirmware.

As mentioned above, the example processes of FIGS. 9-10 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim lists anythingfollowing any form of “include” or “comprise” (e.g., comprises,includes, comprising, including, etc.), it is to be understood thatadditional elements, terms, etc. may be present without falling outsidethe scope of the corresponding claim. As used herein, when the phrase“at least” is used as the transition term in a preamble of a claim, itis open-ended in the same manner as the term “comprising” and“including” are open ended.

FIG. 9 is a flowchart representative of example computer readableinstructions 900 that may be executed to implement the example audiencemeasurement entity of FIGS. 1 and/or 2. Execution of the examplecomputer readable instructions 900 is described in connection with theaudience measurement entity 102 of FIG. 2, but can, in some examples, beapplicable to other data processing facilities.

The data receiver 205 receives RPD from a media provider, such as theexample media provider 110 of FIG. 1 (Block 902). Additionally, the datareceiver 205 sends the RPD to the memory 210. In some examples, theexample data receiver 205 configures the RPD from the media provider foruse by the audience measurement entity 102, while in other examples, theRPD is stored as it is received. The example data receiver 205 canreceive RPD via network communications, physical connection to a memorystorage device, or other similar data communication systems.

The set top box identifier 215 identifies a first group of set top boxesin the RPD that typically include machine events (Block 904) (e.g., arelikely to exhibit machine events). For example, the set top boxidentifier 215 accesses the memory 210, and identifies set top boxesclassified as exhibiting machine events based on their type and modelnumber. In some examples, for a first media provider the example set topbox identifier 215 identifies DVR capable STBs provided by a first mediaprovider and that are known for exhibiting machine events. In someexamples, the set top boxes classified as exhibiting machine events areclassified by analysis of RPD, while other set top boxes are classifiedas exhibiting machine events based on the features of the set top box.However, for a second media provider, the example set top box identifier215 may identify a different model of STB known for exhibiting machineevents.

The tuning event calculator 225 determines a first percentage of set topboxes in the first group of set top boxes that have tuning eventsstarting at a first time (Block 906). For example, if the set top boxidentifier 215 identifies DVR capable STBs of the media provider asbeing classified into the first group of STBs, the tuning eventcalculator 225 calculates the percentage of DVR capable STBs of themedia provider having tuning events during the first time, which, insome examples, covers a short time interval (e.g., 1 second, 5 seconds,1 minute).

The set top box identifier 215 identifies a second group of set topboxes in the RPD that typically do not include machine events (Block908) (e.g., are unlikely to exhibit machine events). For example, theset top box identifier 215 accesses the memory 210, and identifies settop boxes classified as not exhibiting machine events based on theirtype and model number. In some examples, for a first media provider theexample set top box identifier 215 identifies non-DVR capable STBsprovided by the first media provider and that are known for notexhibiting machine events. However, for a second media provider, theexample set top box identifier 215 may identify a different model of STBknown for not exhibiting machine events.

The tuning event calculator 225 determines a second percentage of settop boxes in the second group of set top boxes that have tuning eventsstarting at the first time (Block 910). For example, if the set top boxidentifier 215 identifies non-DVR capable STBs of the media provider asbeing classified into the second group of STBs, the tuning eventcalculator 225 calculates the percentage of non-DVR capable STBs of themedia provider having tuning events, during the first time, which, insome examples, covers a short time interval (e.g., 1 second, 5 seconds,1 minute).

The tuning event calculator 225 determines a ratio of the firstpercentage of set top boxes from the first group having tuning events tothe second percentage of set top boxes from the second group havingtuning events (Block 912). If the ratio satisfies a threshold, theinstructions continue to block 916, otherwise the instructions continueto block 918. If the tuning event calculator 225 determines the ratiohas satisfied the threshold for a sufficient time interval (e.g., 1second, 5 seconds, 15 seconds, etc.), the instructions continue to block920, otherwise, the instructions continue to block 916.

The tuning event calculator 225 flags RPD indicative of a machine event(Block 920). In some examples, the false tuning event rectifier 230rectifies the false tuning events generated by the machine event. Forexample, the false tuning event rectifier 230 may remove all RPDgenerated by STBs exhibiting machine events for the flagged seconds,while in other examples, the false tuning event rectifier 230 determineswhich tuning events are false tuning events and removes only the falsetuning events. In yet other examples, the RPD indicative of a machineevent can be adjusted to correspond to the RPD not indicative of machineevents. In some examples, RPD indicative of a machine event is adjustedby randomly removing set top box tuning events until a percentage oftuning events reported by the group of set top boxes associated withmachine events is within a threshold of the percentage of tuning eventsreported by a group of set top boxes not associated with machine events(e.g., within 50%, within 10%, etc.). In some examples, after the RPDhas been adjusted, the RPD is validated and analyzed to determine theamount of surges or drops for different models or types of STBs hasreached acceptable levels.

The audience measurement entity determines if there is another set ofRPD to process (Block 916). If there is another set of RPD to process,the instructions 900 return to block 902. In some examples, theadditional set of RPD could be for a second time or RPD from a differentmedia provider. Additionally or alternatively, a subset of the firstgroup could evaluated for machine events.

FIG. 10 is a flowchart representative of example computer readableinstructions that may be executed to implement the example audiencemeasurement entity of FIGS. 1 and/or 2. Execution of the examplecomputer readable instructions 1000 is described in connection with theaudience measurement entity 102 of FIG. 2, but can, in some examples, beapplicable to other data processing facilities.

The example instructions 1000 begin when the data receiver 205 receivesa set of RPD data for a first time interval (Block 1002). Additionally,the data receiver 205 sends the RPD to the memory 210. In some examples,the example data receiver 205 configures the RPD from the media providerfor use by the audience measurement entity 102, while in other examples,the RPD is stored as it is received. The example data receiver 205 canreceive RPD via network communications, physical connection to a memorystorage device, or other similar data communication systems.

The set top box identifier 215 identifies a group of set top boxes thattypically include machine events (Block 1004). For example, the set topbox identifier 215 accesses the memory 210, and identifies set top boxesclassified as exhibiting known feature activated machine events based ontheir type and model number. In some examples, for a first mediaprovider the example set top box identifier 215 identifies DVR capableSTBs provided by a first media provider and that are known forexhibiting machine events. However, for a second media provider, theexample set top box identifier 215 may identify a different model of STBknown for exhibiting machine events.

The pattern evaluator 220 evaluates a data pattern in the RPD (Block1006). For example, the pattern evaluator 220 compares the data patternto an identified prerequisite of feature activated machine events. Insome examples, a machine event is restricted to a certain time of day,while in other examples, the machine event could occur at any timeduring the day. Additionally, the pattern evaluator 220 can evaluate RPDfor more than one feature activated machine event.

In some examples, a prerequisite of a feature activated machine event isthe activation of an automatic recording of programs during certaintimes of the day (e.g., automatic recording of primetime shows). Forexample, in response to the recording of a program, a follow-up tuningevent is recorded within a short time of the recording event (e.g., 10seconds, 1 minute, etc.). In some examples, the pattern of a stationcode associated with the recording and a second, follow-up station codeaides in the detection and later rectification of the false tuningevent.

Another example of a prerequisite of a feature activated machine eventincludes viewing a first television program for a first station andviewing a second television program for the first station immediatelypreceding the first television program. In such an example, a falsetuning event can be generated by the machine in between two recordingsat various times of the day. For example, a viewer on channel 1 isviewing the channel 1 programming from 3:01:01 p.m. until 5:25:12 p.m.However at 4:59:45 the STB records an OFF event and at 4:59:45 the STBrecords an ON event. The same second or next second OFF/ON tuning eventis another example prerequisite that aides in the detection and laterrectification of the false tuning event.

While several examples of tuning events patterns indicative of a featureactivated machine event have been disclosed herein, additional patternscan be indicative of feature activated machine events. Regularcomparison of panelist exposure data against RPD is useful to identifydiscrepancies between the veridical tuning data (e.g., panelist exposuredata) and the RPD. Analysis of discrepancies between the veridicaltuning data and the RPD can result in identifying additional patternsindicative of feature activated machine events.

The pattern evaluator 220 flags false tuning data identified as themachine event (Block 1008). In some examples, the pattern evaluator 220only identifies false tuning data produced by the machine event and doesnot flag veridical tuning data. In some examples, the false tuning eventrectifier 230 rectifies (e.g., corrects, removes, etc.) the false tuningevents generated by the machine event. For example, the false tuningevent rectifier 230 may remove all RPD generated by STBs exhibitingmachine events for the flagged seconds, while in other examples, thefalse tuning event rectifier 230 determines which tuning events arefalse tuning events and removes only the false tuning events. In yetother examples, the RPD indicative of a machine event can be adjusted tocorrespond to the RPD not indicative of machine events. In someexamples, RPD indicative of a machine event is adjusted by randomlyremoving set top box tuning events until a percentage of tuning eventsreported by the group of set top boxes associated with machine events iswithin a threshold of the percentage of tuning events reported by agroup of set top boxes not associated with machine events (e.g., within50%, within 10%, etc.). In some examples, after the RPD has beenadjusted, the RPD is validated and analyzed to determine the amount ofsurges or drops for different models or types of STBs has reachedacceptable levels.

The audience measurement entity 102 determines if there is another setof RPD to process (Block 1010). If there is another set of RPD toprocess, the instructions 900 return to block 902. In some examples, theadditional set of RPD could be for a second time or RPD from a differentmedia provider. Additionally or alternatively, a subset of the firstgroup could evaluated for machine events.

FIG. 11 is a block diagram of an example processor platform 1100 capableof executing the instructions of FIGS. 9-10 to implement the exampleaudience measurement entity 102 of FIG. 2. The processor platform 1100can be, for example, a server, a personal computer, a mobile device(e.g., a cell phone, a smart phone, a tablet such as an iPad™), anInternet appliance, a DVD player, a digital video recorder, a Blu-rayplayer, a gaming console, a set top box, or any other type of computingdevice.

The processor platform 1100 of the illustrated example includes aprocessor 1112. The processor 1112 of the illustrated example ishardware. For example, the processor 1112 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer. The hardware processor may be asemiconductor based (e.g., silicon based) device. In this example, theprocessor implements data receiver 205, the example set top boxidentifier 215, the example pattern evaluator 220, the example tuningevent calculator 225, and the example false tuning event rectifier 230.

The processor 1112 of the illustrated example includes a local memory1113 (e.g., a cache). The processor 1112 of the illustrated example isin communication with a main memory including a volatile memory 1114 anda non-volatile memory 1116 via a bus 1118. The volatile memory 1114 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 1116 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 1114,1116 is controlled by a memory controller.

The processor platform 1100 of the illustrated example also includes aninterface circuit 1120. The interface circuit 1120 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1122 are connectedto the interface circuit 1120. The input device(s) 1122 permit(s) a userto enter data and/or commands into the processor 1112. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1124 are also connected to the interfacecircuit 1120 of the illustrated example. The output devices 1024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 1120 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip, and/or a graphics driver processor.

The interface circuit 1120 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1126 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1100 of the illustrated example also includes oneor more mass storage devices 1128 for storing software and/or data.Examples of such mass storage devices 1128 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives. In this example, themass storage device includes the example memory 210.

The coded instructions 1132 of FIGS. 9-10 may be stored in the massstorage device 1128, in the volatile memory 1114, in the non-volatilememory 1116, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that detectand/or rectify false set top box tuning data. False tuning data isdetermined by comparing STB tuning data to trustworthy RPD to determinemachine events and/or by evaluating patterns in collected tuning data.Rectification of RPD improves the field of STB media monitoring byimproving the accuracy of data collected. Rectified RPD reduces theamount of processing power required to augment RPD data to correlatewith panelist data. Additionally, by removing false tuning events,computers processing RPD require lower memory requirements and lessprocessing power.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus to process return path data from settop boxes, the apparatus comprising: a set top box identifier toidentify, in the return path data, tuning data corresponding to a firstgroup of set top boxes, the first group of set top boxes classified asassociated with machine events; a pattern evaluator to identify a firsttime interval of the tuning data, the first time interval of the tuningdata including a portion of the tuning data defining a patternindicative of an occurrence of at least one of the machine eventsassociated with the first group of set top boxes, the first timeinterval being longer than a duration of the at least one of the machineevents; and a false tuning event rectifier to remove, from the returnpath data, the tuning data associated with the first time interval toreduce an amount of tuning events in the return path data that resultfrom the machine events, the removed tuning data including both falsetuning data and veridical tuning data contained in the return path data.2. The apparatus of claim 1, wherein the pattern evaluator is toidentify a second time interval during which a difference between (i) afirst percentage of tuning events attributed to the first group of theset top boxes in the return path data and (ii) a second percentage oftuning events attributed to a second group of the set top boxes in thereturn path data satisfies a threshold.
 3. The apparatus of claim 2,wherein the tuning data is first tuning data, and the false tuning eventrectifier is to remove second tuning data associated with the secondtime interval from the return path data, the second tuning datacorresponding to the first group of set top boxes.
 4. The apparatus ofclaim 2, wherein the threshold is to vary across different periods oftime.
 5. The apparatus of claim 1, wherein the pattern is indicative ofat least one feature activated machine event.
 6. The apparatus of claim5, wherein the false tuning event rectifier is to identify the featureactivated machine event based on a prerequisite of the feature activatedmachine event.
 7. The apparatus of claim 1, wherein the tuning data isfirst tuning data, the pattern evaluator is to: determine a ratiobetween first tuning events, in the return path data, attributed to thefirst group of the set top boxes and second tuning events, in the returnpath data, attributed to a second group of the set top boxes; andidentify a second time interval of the return path data during which theratio satisfies a threshold, the false tuning event rectifier to: removesecond tuning data associated with the second time interval from thereturn path data, the second tuning data corresponding to the firstgroup of set top boxes.
 8. A method to process return path data from settop boxes, the method comprising: identifying, by executing aninstruction with a processor, in the return path data tuning datacorresponding to a first group of set top boxes, the first group of settop boxes classified as associated with machine events; identifying, byexecuting an instruction with the processor, a first time interval ofthe tuning data, the first time interval of the tuning data including aportion of the tuning data defining a pattern indicative of anoccurrence of at least one of the machine events associated with thefirst group of set top boxes, the first time interval being longer thana duration of the at least one of the machine events; and removing, fromthe return path data, the tuning data associated with the first timeinterval to reduce an amount of tuning events in the return path datathat result from the machine events, the removed tuning data includingboth false tuning data and veridical tuning data contained in the returnpath data.
 9. The method of claim 8, further including identifying asecond time interval during which a difference between (i) a firstpercentage of tuning events attributed to the first group of the set topboxes in the return path data and (ii) a second percentage of tuningevents attributed to a second group of the set top boxes in the returnpath data satisfies a threshold.
 10. The method of claim 9, wherein thetuning data is first tuning data, and further including removing secondtuning data associated with the second time interval from the returnpath data, the second tuning data corresponding to the first group ofset top boxes.
 11. The method of claim 9, wherein the threshold variesacross different periods of time.
 12. The method of claim 8, wherein thepattern is indicative of at least one feature activated machine event.13. The method of claim 12, wherein the feature activated machine eventis identified based on a prerequisite of the feature activated machineevent.
 14. The method of claim 8, wherein the tuning data is firsttuning data, and further including: determining a ratio between firsttuning events, in the return path data, attributed to the first group ofthe set top boxes and second tuning events, in the return path data,attributed to a second group of the set top boxes; identifying a secondtime interval of the return path data during which the ratio satisfies athreshold; and removing second tuning data associated with the secondtime interval from the return path data, the second tuning datacorresponding to the first group of set top boxes.
 15. A non-transitorycomputer readable medium comprising computer readable instructionswhich, when executed, cause a processor to at least: identify, in returnpath data, tuning data corresponding to a first group of set top boxes,the first group of set top boxes classified as associated with machineevents; identify a first time interval of the tuning data, the firsttime interval of the tuning data including a portion of the tuning datadefining a pattern indicative of an occurrence of at least one of themachine events associated with the first group of set top boxes, thefirst time interval being longer than a duration of the at least one ofthe machine events; and remove, from the return path data, the tuningdata associated with the first time interval to reduce an amount oftuning events in the return path data that result from the machineevents, the removed tuning data including both false tuning data andveridical tuning data contained in the return path data.
 16. Thenon-transitory computer readable medium of claim 15, wherein theinstructions cause the processor to identify a second time intervalduring which a difference between (i) a first percentage of tuningevents attributed to the first group of the set top boxes in the returnpath data and (ii) a second percentage of tuning events attributed to asecond group of the set top boxes in the return path data satisfies athreshold.
 17. The non-transitory computer readable medium of claim 16,wherein the tuning data is first tuning data, and the instructions causethe processor to remove second tuning data associated with the secondtime interval from the return path data, the second tuning datacorresponding to the first group of set top boxes.
 18. Thenon-transitory computer readable medium of claim 16, wherein thethreshold is to vary across different periods of time.
 19. Thenon-transitory computer readable medium of claim 15, wherein the patternis indicative of at least one feature activated machine event.
 20. Thenon-transitory computer readable medium of claim 15, wherein the tuningdata is first tuning data, and the instructions cause the processor to:determine a ratio between first tuning events, in the return path data,attributed to the first group of the set top boxes and second tuningevents, in the return path data, attributed to a second group of the settop boxes; identify a second time interval of the return path dataduring which the ratio satisfies a threshold; and remove second tuningdata associated with the second time interval from the return path data,the second tuning data corresponding to the first group of set topboxes.
 21. An apparatus comprising: memory; instructions; and at leastone processor to execute the instructions to: identify, in return pathdata, tuning data corresponding to a first group of set top boxes, thefirst group of set top boxes classified as associated with machineevents; identify a first time interval of the tuning data, the firsttime interval of the tuning data including a portion of the tuning datadefining a pattern indicative of an occurrence of at least one of themachine events associated with the first group of set top boxes, thefirst time interval being longer than a duration of the at least one ofthe machine events; and remove, from the return path data, the tuningdata associated with the first time interval to reduce an amount oftuning events in the return path data that result from the machineevents, the removed tuning data including both false tuning data andveridical tuning data contained in the return path data.
 22. Theapparatus of claim 21, wherein the at least one processor is to identifya second time interval during which a difference between (i) a firstpercentage of tuning events attributed to the first group of the set topboxes in the return path data and (ii) a second percentage of tuningevents attributed to a second group of the set top boxes in the returnpath data satisfies a threshold.
 23. The apparatus of claim 22, whereinthe tuning data is first tuning data, and the at least one processor isto remove second tuning data associated with the second time intervalfrom the return path data, the second tuning data corresponding to thefirst group of set top boxes.
 24. The apparatus of claim 22, wherein thethreshold is to vary across different periods of time.
 25. The apparatusof claim 21, wherein the pattern is indicative of at least one featureactivated machine event.
 26. The apparatus of claim 21, wherein thetuning data is first tuning data, and the at least one processor is to:determine a ratio between first tuning events, in the return path data,attributed to the first group of the set top boxes and second tuningevents, in the return path data, attributed to a second group of the settop boxes; identify a second time interval of the return path dataduring which the ratio satisfies a threshold; and remove second tuningdata associated with the second time interval from the return path data,the second tuning data corresponding to the first group of set topboxes.